Seeking to unlock the inner workings of the brain… from behind a computer

In a universe with parallel lives, we could have found Christian Machens hidden behind a pile of books, immersed in the plot of his most recent novel. Or leaning over a piano compulsively searching for the last set of tones of his newest symphony. But at the age of 18, Christian decided to take the “safe bet”, in his own words, and study physics.
Today, Christian Machens is a Principal Investigator at the Champalimaud Centre for the Unknown, in search for the key to crack the code of how the brain works…from behind a computer. Together with his team, he has been looking at populations of hundreds, thousands, tens of thousands of neurons, trying to understand their organization and function, and how the joint activity of these large ensembles of cells contribute to behavior.

It was a sunny October afternoon. Christian was waiting for us in his bright contemplative office, surrounded by books on Neuroscience and Mathematics. On his office door, a revealing sign, with the words ‘Theoretical Neuroscience’ written on it.

So, what is a theoretical neuroscientist? And how is your daily life?

Humm…Basically, we spend our days looking at data on the computer, thinking very hard about it and then trying to come up with some description, some model, of that particular type of data.
There is a story from the field of physics that I like, and that I think is a nice representation of what we do. Tycho Brahe was a Danish astrophysicist, who spent 20 or 30 years of his life recording the positions of the stars, planets, moon, and sun, on every possible night and day, and carefully recording these measurements and writing them down in a book. After all those years, he had this huge quantity of data about the positions of objects in the sky, but it was very hard to make sense of it. During the late phase of his career, Brahe met Johannes Kepler, a German physicist, who started working as his assistant. Kepler was driven to find an explanation for the order of the planets and their movements across the sky, and he was convinced that it could be explained through mathematical calculation and systematic thinking. And that’s what happened – after many years of hard work delving into Brahe’s notes, he came up with a simple model of how planets rotate around the sun following an elliptical orbit. Can you imagine the crazy amount of work it must had been to go through all the recorded data?
So, going back to the initial question of what is a theoretical (neuro)scientist…I think that Kepler’s story and his efforts in trying to find the simplest mathematical description of Brahe’s observations, through calculation and by testing one idea after another, are a good metaphor for what we do in our daily lives. Except nowadays, we have computers. Fortunately.

Could you give us an example from your work?

Imagine you have recorded from 10.000 neurons at the same time, and then you want to understand what this ensemble of cells is doing: to which sensory stimuli these neurons are responding to and how their joint activity will contribute to behavior.
The first problem you encounter is that not all the neurons from this ensemble do the same thing. On top of this, some of these individual cells respond to many different things. For example, imagine that an animal has to select one of two objects to obtain a reward. There will be neurons in specific structures of the brain that will respond both to the object presented to the animal, the animal’s decision, and the reward, displaying what has been termed mixed selectivity. So, given this complexity, how can one investigate what this population of neurons is doing? One approach could be looking at each neuron individually, one by one, but that would take an enormous amount of time.

As in the case of Tycho Brahe’s observations…

Exactly. So, this is a big challenge. First, we have to be able to visualize the data in a way that allows us to observe what’s going on. Taking the example above, we need a method to ‘demix’ the neural activity, to break it down into its individual components, so that we can then relate it to single aspects of the task, like the object presented to the animal, the animal’s decision, or the reward. And second, we need to come up with a mathematical description that will allow us to understand what this population of neurons is doing and how it will contribute to behavior.

As Johannes Kepler did for the data from the movements of the planets…

Yes. Moreover, and very importantly, having a mathematical description has the super-important property of allowing us to make predictions. For example, the next time you perform an experiment and record from a different set of neurons, you’ll be able to predict the responses of these new neurons based on the model developed before. And this is exactly one of the goals of our work – to be able to predict, in a new experiment, what we are going to observe. Most likely, we won’t be able to predict every single detail, because there’s individuality. But despite this, there are patterns in the brain that turn out to be common across different individuals performing the same behavioral task – and that’s what we want to be able to predict.

Why did you decide to study these questions?

I think coincidence was a big factor. I actually studied physics in college, not neuroscience. So, how does a physicist end up in neuroscience? After finishing college, I wanted to work in theoretical physics, specifically elementary particle physics. There’s a lot of work one could do in that field, but I realized that none of it could be tested in a short timescale. What do I mean by this? Imagine that you are working in theoretical particle physics and you’d like to test a new idea or hypothesis. For that, you’d have to run it in CERN, which would cost billions of dollars. So basically, your chances of getting your theory tested are extremely slim, and I wanted to study something closer to reality.
At the time, I was living in Berlin, and a group of scientists, including some physicists, had started a new institute for theoretical biology. Their research programme sounded exciting, with testable questions in biology that could be developed or tested within a timeframe of months rather than tens of years as in theoretical physics. So I decided to join this institute, which was full of young, dynamic and approachable researchers, and this was my entry point into neuroscience, where I studied the auditory system of grasshoppers. From then on, I started exploring the world of neuroscience, and basically I was driven by whatever else popped out that I found interesting.

Looking back at your career, could you pinpoint some personality traits you can now see were important for your path as a scientist?

I think you need to be smart to a certain extent, but you don’t need to be the smartest. For instance, at university I was in the top 10-20% range of the students taking physics, but I was not the star student.
In my opinion, the most important feature is: you have to be driven, you have to want to figure out how nature works and how to get to the bottom of things. And to be somehow unsatisfied with what is already known. I guess this comes from a thirst for knowledge. This might sound trivial, but my point is that this thirst for knowledge has to rank above pretty much everything else, such as wanting to be right or to look smart. And during the tough phases, which will happen, this drive will be a fundamental strength for you to keep going as a scientist.
Following this career also comes with important practical implications. Your salary, for instance, is going to be a lot lower compared with your colleagues that had a similar education but pursued a career outside academia. On the other hand, you have to be willing to move around. In my case, I studied in two different places in Germany, and I lived in the US for two different periods of time: first, one year as a student, and later, four years as a postdoc. After that, I moved to Germany as a Principal Investigator, then to Paris, and now I’m in Lisbon. So I moved around a lot, maybe more than the average scientist, but I also profited a lot from that, because in each new environment you get a lot of new impulses that you’ll simply never get by attending conferences and so on. So, being in many different laboratories, with different challenges and different people, is a huge plus.

As a scientist, what do you think is the most important message you have to convey?

Be skeptical. Especially about your own beliefs, and about the beliefs of the people around you. Question that all the time.

Do you think you’ll want to be a scientist forever and ever?

By now, I’d say yes. But that’s because you can’t have parallel lives. As you grow older, it becomes a little harder to start anew. You accumulate a lot of knowledge. And I don’t think we’d be able to compete with the people in their 20’s that are starting now. But maybe I’m just being old-fashioned.

Maria Inês Vicente works at the Science Communication Office at the Champalimaud Research